In particular, drugs sharing side effects tend to bind to common protein targets [ 8] and mouse models of functionally related genes often show similar phenotypes [ 9].
These TGF-βs isoforms bind to common specific transmembrane receptors (TGF-β receptypesypes I and II) to target genes via the SMAD family of signal transducing proteins [ 22].
The enrichment of HuR-binding sites in ADAR targets and the identification of an RNA-dependent HuR-ADAR complex led us to reason that HuR and ADAR bind to common transcripts and regulate them cooperatively.
This class of genes is distinct from common lamin A and progerin targets by a number of characteristics: First, progerin targets are involved in different biological processes as compared to common targets, including the general active pathway of proteolysis, and are less enriched for TFMs enriched in promoters bound by both lamin A and progerin.
Thus, ELMs and SLiMs are both identified as sequence patterns in multiple proteins that bind to a common target, with the SLiM-containing set likely to be entirely nonhomologous but with no such restriction on the ELM-containing set.
Thus, ELMs and SLiMs are both identified as sequence patterns in multiple proteins that bind to a common target, with the SLiM-containing set likely to be entirely non-homologous but with no such restriction on the ELM-containing set.
Both drugs bind to a common target PTGS2, which may play a role as a major mediator of inflammation and/or a role for prostanoid signaling in activity-dependent plasticity [ 43].
After that, machine learning techniques such as data cleaning, dimension reduction, and ensemble classifier were used to prioritize drug pairs bound to a common target.
After that, machine learning techniques were adopted to prioritize drug pairs bound to a common target (see the Methods section for the detailed procedures).
However, the weak point of this method is that it cannot consider many structurally unrelated drugs bound to a common target [ 18, 19].
